{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--BOOK_INFORMATION-->\n",
    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
    "\n",
    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
    "\n",
    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) | [Contents](Index.ipynb) | [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) >\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.01-Simple-Line-Plots.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Line Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perhaps the simplest of all plots is the visualization of a single function $y = f(x)$.\n",
    "Here we will take a first look at creating a simple plot of this type.\n",
    "As with all the following sections, we'll start by setting up the notebook for plotting and  importing the packages we will use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For all Matplotlib plots, we start by creating a figure and an axes.\n",
    "In their simplest form, a figure and axes can be created as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Matplotlib, the *figure* (an instance of the class ``plt.Figure``) can be thought of as a single container that contains all the objects representing axes, graphics, text, and labels.\n",
    "The *axes* (an instance of the class ``plt.Axes``) is what we see above: a bounding box with ticks and labels, which will eventually contain the plot elements that make up our visualization.\n",
    "Throughout this book, we'll commonly use the variable name ``fig`` to refer to a figure instance, and ``ax`` to refer to an axes instance or group of axes instances.\n",
    "\n",
    "Once we have created an axes, we can use the ``ax.plot`` function to plot some data. Let's start with a simple sinusoid:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "\n",
    "x = np.linspace(0, 10, 1000)\n",
    "ax.plot(x, np.sin(x));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we can use the pylab interface and let the figure and axes be created for us in the background\n",
    "(see [Two Interfaces for the Price of One](04.00-Introduction-To-Matplotlib.ipynb#Two-Interfaces-for-the-Price-of-One) for a discussion of these two interfaces):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to create a single figure with multiple lines, we can simply call the ``plot`` function multiple times:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x))\n",
    "plt.plot(x, np.cos(x));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's all there is to plotting simple functions in Matplotlib!\n",
    "We'll now dive into some more details about how to control the appearance of the axes and lines."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adjusting the Plot: Line Colors and Styles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first adjustment you might wish to make to a plot is to control the line colors and styles.\n",
    "The ``plt.plot()`` function takes additional arguments that can be used to specify these.\n",
    "To adjust the color, you can use the ``color`` keyword, which accepts a string argument representing virtually any imaginable color.\n",
    "The color can be specified in a variety of ways:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x - 0), color='blue')        # specify color by name\n",
    "plt.plot(x, np.sin(x - 1), color='g')           # short color code (rgbcmyk)\n",
    "plt.plot(x, np.sin(x - 2), color='0.75')        # Grayscale between 0 and 1\n",
    "plt.plot(x, np.sin(x - 3), color='#FFDD44')     # Hex code (RRGGBB from 00 to FF)\n",
    "plt.plot(x, np.sin(x - 4), color=(1.0,0.2,0.3)) # RGB tuple, values 0 to 1\n",
    "plt.plot(x, np.sin(x - 5), color='chartreuse'); # all HTML color names supported"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If no color is specified, Matplotlib will automatically cycle through a set of default colors for multiple lines.\n",
    "\n",
    "Similarly, the line style can be adjusted using the ``linestyle`` keyword:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x + 0, linestyle='solid')\n",
    "plt.plot(x, x + 1, linestyle='dashed')\n",
    "plt.plot(x, x + 2, linestyle='dashdot')\n",
    "plt.plot(x, x + 3, linestyle='dotted');\n",
    "\n",
    "# For short, you can use the following codes:\n",
    "plt.plot(x, x + 4, linestyle='-')  # solid\n",
    "plt.plot(x, x + 5, linestyle='--') # dashed\n",
    "plt.plot(x, x + 6, linestyle='-.') # dashdot\n",
    "plt.plot(x, x + 7, linestyle=':');  # dotted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you would like to be extremely terse, these ``linestyle`` and ``color`` codes can be combined into a single non-keyword argument to the ``plt.plot()`` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v3pjUvLnQEE+7mobJP05G5+WdoTVokfRKEr566iurD3GAV+REYul0wPbtwJNPqsfDhwMj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1077be9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, x + 0, '-g')  # solid green\n",
    "plt.plot(x, x + 1, '--c') # dashed cyan\n",
    "plt.plot(x, x + 2, '-.k') # dashdot black\n",
    "plt.plot(x, x + 3, ':r');  # dotted red"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These single-character color codes reflect the standard abbreviations in the RGB (Red/Green/Blue) and CMYK (Cyan/Magenta/Yellow/blacK) color systems, commonly used for digital color graphics.\n",
    "\n",
    "There are many other keyword arguments that can be used to fine-tune the appearance of the plot; for more details, I'd suggest viewing the docstring of the ``plt.plot()`` function using IPython's help tools (See [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adjusting the Plot: Axes Limits\n",
    "\n",
    "Matplotlib does a decent job of choosing default axes limits for your plot, but sometimes it's nice to have finer control.\n",
    "The most basic way to adjust axis limits is to use the ``plt.xlim()`` and ``plt.ylim()`` methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x))\n",
    "\n",
    "plt.xlim(-1, 11)\n",
    "plt.ylim(-1.5, 1.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If for some reason you'd like either axis to be displayed in reverse, you can simply reverse the order of the arguments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x))\n",
    "\n",
    "plt.xlim(10, 0)\n",
    "plt.ylim(1.2, -1.2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A useful related method is ``plt.axis()`` (note here the potential confusion between *axes* with an *e*, and *axis* with an *i*).\n",
    "The ``plt.axis()`` method allows you to set the ``x`` and ``y`` limits with a single call, by passing a list which specifies ``[xmin, xmax, ymin, ymax]``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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F09smGPZq0QJ48klgyRLZScge8+eL7jRvb9lJnGvcOHGF4spY1GuxbRtwyy3An/8sO4nz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107508b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x))\n",
    "plt.axis([-1, 11, -1.5, 1.5]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ``plt.axis()`` method goes even beyond this, allowing you to do things like automatically tighten the bounds around the current plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Wqpw40aWn0a1GjaQlsnGjuWc5UsWWLpWGAZUtPl4GN+i50Ot2INWmTfLiqHFj1Um0a/Bg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\nBk/pWBptAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107d1c9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x))\n",
    "plt.axis('tight');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It allows even higher-level specifications, such as ensuring an equal aspect ratio so that on your screen, one unit in ``x`` is equal to one unit in ``y``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HH8sBr+QfTCY5o7FvXxlq0W1Piarm1CkJ8dRU+bkxxP1Hu3bSSX3qKemhq2Ro+UBERARK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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1076ded30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x))\n",
    "plt.axis('equal');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more information on axis limits and the other capabilities of the ``plt.axis`` method, refer to the ``plt.axis`` docstring."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Labeling Plots\n",
    "\n",
    "As the last piece of this section, we'll briefly look at the labeling of plots: titles, axis labels, and simple legends.\n",
    "\n",
    "Titles and axis labels are the simplest such labels—there are methods that can be used to quickly set them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+/hw/fpxLly7h5eXFnj17GDFixE2P5efnV+TzW1FSUpLhrkXNmuDtDb//7keL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\nIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107d57438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x))\n",
    "plt.title(\"A Sine Curve\")\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"sin(x)\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The position, size, and style of these labels can be adjusted using optional arguments to the function.\n",
    "For more information, see the Matplotlib documentation and the docstrings of each of these functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When multiple lines are being shown within a single axes, it can be useful to create a plot legend that labels each line type.\n",
    "Again, Matplotlib has a built-in way of quickly creating such a legend.\n",
    "It is done via the (you guessed it) ``plt.legend()`` method.\n",
    "Though there are several valid ways of using this, I find it easiest to specify the label of each line using the ``label`` keyword of the plot function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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u3r2Kd6g37775LqObjla7HLNzcIBatQyP9XrDKUv7F6+nnqfVLlWbn/r8ROuw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\nXAghNO7/ADRl1uEb30mBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107d7ca90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, np.sin(x), '-g', label='sin(x)')\n",
    "plt.plot(x, np.cos(x), ':b', label='cos(x)')\n",
    "plt.axis('equal')\n",
    "\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the ``plt.legend()`` function keeps track of the line style and color, and matches these with the correct label.\n",
    "More information on specifying and formatting plot legends can be found in the ``plt.legend`` docstring; additionally, we will cover some more advanced legend options in [Customizing Plot Legends](04.06-Customizing-Legends.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aside: Matplotlib Gotchas\n",
    "\n",
    "While most ``plt`` functions translate directly to ``ax`` methods (such as ``plt.plot()`` → ``ax.plot()``, ``plt.legend()`` → ``ax.legend()``, etc.), this is not the case for all commands.\n",
    "In particular, functions to set limits, labels, and titles are slightly modified.\n",
    "For transitioning between MATLAB-style functions and object-oriented methods, make the following changes:\n",
    "\n",
    "- ``plt.xlabel()``  → ``ax.set_xlabel()``\n",
    "- ``plt.ylabel()`` → ``ax.set_ylabel()``\n",
    "- ``plt.xlim()``  → ``ax.set_xlim()``\n",
    "- ``plt.ylim()`` → ``ax.set_ylim()``\n",
    "- ``plt.title()`` → ``ax.set_title()``\n",
    "\n",
    "In the object-oriented interface to plotting, rather than calling these functions individually, it is often more convenient to use the ``ax.set()`` method to set all these properties at once:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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i7Y7UVMDBQXYaKo8lSwA/P7EM7OOPa7PPUhWI//znP+bOQTbslVfE3DJJScDg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\nIiJSxQJBRESqWCCIiEgVCwQREan6f8M6gCXBCtQMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107f67cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes()\n",
    "ax.plot(x, np.sin(x))\n",
    "ax.set(xlim=(0, 10), ylim=(-2, 2),\n",
    "       xlabel='x', ylabel='sin(x)',\n",
    "       title='A Simple Plot');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Visualization with Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) | [Contents](Index.ipynb) | [Simple Scatter Plots](04.02-Simple-Scatter-Plots.ipynb) >\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.01-Simple-Line-Plots.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
